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Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations. 

Abstract Dissolved organic carbon (DOC) is a key variable impacting stream biogeochemical processes. The relationship between DOC concentration (C) and stream discharge (q) can elucidate spatial and temporal DOC source dynamics in watersheds. In the ephemeral glacial meltwater streams of the McMurdo Dry Valleys (MDV), Antarctica, the C‐qrelationship has been applied to dissolved inorganic nitrogen and weathering solutes including silica, which all exhibit chemostatic C‐qbehavior; but DOC‐qdynamics have not been studied. DOC concentrations here are low compared to temperate streams, in the range of 0.1–2 mg C l⁻¹, and their chemical signal clearly indicates derivation from microbial biomass (benthic mats and hyporheic biofilm). To investigate whether the DOC generation rate from these autochthonous organic matter pools was sufficient to maintain chemostasis for DOC, despite these streams' large diel and interannual fluctuations in discharge, we fit the long‐term DOC‐qdata to a power law and an advection‐reaction model. Model outputs and coefficients of variation characterize the DOC‐qrelationship as chemostatic for several MDV streams. We propose a conceptual model in which hyporheic carbon storage, hyporheic exchange rates, and net DOC generation rates are key interacting components that enable chemostatic DOC‐qbehavior in MDV streams. This model clarifies the role of autochthonous carbon stores in maintaining DOC chemostasis and may be useful for examining these relationships in temperate systems, which typically have larger sources of bioavailable autochthonous organic carbon than MDV streams but where this autochthonous signal could be masked by a stronger allochthonous contribution. 

The opportunity to participate in and contribute to emerging fields is increasingly prevalent in science. However, simply thinking about stepping outside of your academic silo can leave many students reeling from the uncertainty. Here, we describe 10 simple rules to successfully train yourself in an emerging field, based on our experience as students in the emerging field of ecological forecasting. Our advice begins with setting and revisiting specific goals to achieve your academic and career objectives and includes several useful rules for engaging with and contributing to an emerging field.